Comparing foreground removal techniques for recovery of the LOFAR-EoR 21 cm power spectrum. Issue 2 (12th November 2020)

• Record Type: Journal Article
• Authors: Hothi, Ian; Chapman, Emma; Pritchard, Jonathan R; Mertens, F G; Koopmans, L V E; Ciardi, B; Gehlot, B K; Ghara, R; Ghosh, A; Giri, S K; Iliev, I T; Jelić, V; Zaroubi, S
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 500:Issue 2(2021)
• Page Start: 2264
• Page End: 2277
• Publication Date: 2020-11-12
• Subjects: methods: data analysis -- methods: statistical -- techniques: interferometric -- cosmology: observations -- cosmology: theory -- dark ages, reionization, first stars; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/staa3446
• Languages: English
• ISSNs: 0035-8711

Abstract

ABSTRACT: We compare various foreground removal techniques that are being utilized to remove bright foregrounds in various experiments aiming to detect the redshifted 21 cm signal of neutral hydrogen from the epoch of reionization. In this work, we test the performance of removal techniques (FastICA, GMCA, and GPR) on 10 nights of LOFAR data and investigate the possibility of recovering the latest upper limit on the 21 cm signal. Interestingly, we find that GMCA and FastICA reproduce the most recent 2σ upper limit of $\Delta ^2_{21} \lt $ (73) 2 mK 2 at k = 0.075 h cMpc −1, which resulted from the application of GPR. We also find that FastICA and GMCA begin to deviate from the noise-limit at k -scales larger than ∼0.1 h cMpc −1 . We then replicate the data via simulations to see the source of FastICA and GMCA's limitations, by testing them against various instrumental effects. We find that no single instrumental effect, such as primary beam effects or mode-mixing, can explain the poorer recovery by FastICA and GMCA at larger k -scales. We then test scale-independence of FastICA and GMCA, and find that lower k -scales can be modelled by a smaller number of independent components. For larger scales ( k ≳ 0.1 h cMpc −1 ), more independent components are needed to fit the foregrounds. We conclude that, the current usage of GPR by the LOFAR collaboration is the appropriate removal technique. It is both robust and less prone to overfitting, with future improvements to GPR's fitting optimization to yield deeper limits.